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Qingyun Zhao and Frederick H. Carr

Abstract

An explicit cloud prediction model has been developed and incorporated into the Eta Model at the National Centers for Environmental Prediction. In this scheme, only one predictive variable, cloud mixing ratio, is added to the model’s prognostic equations to represent both cloud liquid water and cloud ice. Precipitation is diagnostically calculated from cloud mixing ratio. Extensive tests have been performed. The statistical results show a significant improvement in the model precipitation forecasts. Diagnostic studies suggest that the inclusion of cloud ice is important in transferring water vapor to precipitation and in the enhancement of latent heat release; the latter subsequently affects the vertical motion field significantly.

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Ming Liu, Young-Joon Kim, and Qingyun Zhao

Abstract

A high-order accurate radiative transfer (RT) model developed by Fu and Liou has been implemented into the Navy Operational Global Atmospheric Prediction System (NOGAPS) to improve the energy budget and forecast skill. The Fu–Liou RT model is a four-stream algorithm (with a two-stream option) integrating over 6 shortwave bands and 12 longwave bands. The experimental 10-day forecasts and analyses from data assimilation cycles are compared with the operational output, which uses a two-stream RT model of three shortwave and five longwave bands, for both winter and summer periods. The verifications against observations of radiosonde and surface data show that the new RT model increases temperature accuracy in both forecasts and analyses by reducing mean bias and root-mean-square errors globally. In addition, the forecast errors also grow more slowly in time than those of the operational NOGAPS because of accumulated effects of more accurate cloud–radiation interactions. The impact of parameterized cloud effective radius in estimating liquid and ice water optical properties is also investigated through a sensitivity test by comparing with the cases using constant cloud effective radius to examine the temperature changes in response to cloud scattering and absorption. The parameterization approach is demonstrated to outperform that of constant radius by showing smaller errors and better matches to observations. This suggests the superiority of the new RT model relative to its operational counterpart, which does not use cloud effective radius. An effort has also been made to improve the computational efficiency of the new RT model for operational applications.

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Qingyun Zhao, Tracy Haack, Justin McLay, and Carolyn Reynolds

Abstract

An ensemble forecast system has been developed at the Naval Research Laboratory to improve the analyses and forecasts of atmospheric refractivity for electromagnetic (EM) propagation with the intention of accounting for uncertainties in model forecast errors. Algorithms for a matrix of ensemble statistics have been developed to analyze the probability, location, intensity, and structure of ducting of various types. Major parameters of ducting layers and their ensemble statistics are calculated from the ensemble forecasts. Their relationships to the large-scale and mesoscale environment are also investigated. The Wallops Island field experiment from late April to early May 2000 is selected to evaluate the system. During the spring season, this coastal region maintains a strong sea surface temperature gradient between cold shelf waters and the warm Gulf Stream, where the boundaries between land, the coastal water, and the Gulf Stream have a strong influence on marine boundary layer structures and the formation of ducting layers. Sounding profiles during the field experiment are used in the study to further understand the structures of the ducting layers and also to validate the ensemble forecast system. While some advantages of the ensemble system over the deterministic forecast for atmospheric refractivity prediction in the boundary layer are studied and demonstrated in this study, the weaknesses of the current ensemble system are revealed for future improvement of the system.

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Qingyun Zhao, John Cook, Qin Xu, and Paul R. Harasti

Abstract

A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrained by the model’s dynamics and properly adjusted to satisfy the model’s thermodynamical balance.

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Qingyun Zhao, Qin Xu, Yi Jin, Justin McLay, and Carolyn Reynolds

Abstract

The time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy’s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model’s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.

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Qingyun Zhao, John Cook, Qin Xu, and Paul R. Harasti

Abstract

A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

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Qin Xu, Li Wei, Yi Jin, Qingyun Zhao, and Jie Cao

Abstract

This paper proposes a new method to properly define and accurately determine the vortex center of a model-predicted tropical cyclone (TC) from a dynamic perspective. Ideally, a dynamically determined TC vortex center should maximize the gradient wind balance or, equivalently, minimize the gradient wind imbalance measured by an energy norm over the TC vortex. In practice, however, such an energy norm cannot be used to easily and unambiguously determine the TC vortex center. An alternative yet practical approach is developed to dynamically and unambiguously define the TC vortex center. In this approach, the TC vortex core of near-solid-body rotation is modeled by a simple parametric vortex constrained by the gradient wind balance. Therefore, the modeled vortex can fit simultaneously the perturbation pressure and streamfunction of the TC vortex part (extracted from the model-predicted fields) over the TC vortex core area (within the radius of maximum tangential wind), while the misfit is measured by a properly defined cost function. Minimizing this cost function yields the desired dynamic optimality condition that can uniquely define the TC vortex center. Using this dynamic optimality condition, a new method is developed in the form of iterative least squares fit to accurately determine the TC vortex center. The new method is shown to be efficient and effective for finding the TC vortex center that accurately satisfies the dynamic optimality condition.

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Lei Zhang, Zhaoxia Pu, Wen-Chau Lee, and Qingyun Zhao

Abstract

The impact of airborne Doppler radar data assimilation on improving numerical simulations of tropical cyclones (TCs) has been well recognized. However, the influence of radar data quality on the numerical simulation of tropical cyclones has not been given much attention. It is commonly assumed that higher quality radar data would be more beneficial to numerical simulations of TCs. This study examines the impact of the radar data quality control on assimilation of the airborne Doppler radar reflectivity and radial velocity observations in a numerical simulation of Typhoon Jangmi (2008). It is found that the quality of radar data has a strong influence on the numerical simulation of Typhoon Jangmi in terms of its track, intensity, and precipitation structures. Specifically, results suggest that a trade-off between the data quality and data coverage is necessary for different purposes in practical applications, as the higher quality data contribute to intensity forecast improvements, whereas data of lower quality but having better coverage are more beneficial to accurate track forecasting.

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Qingyun Zhao, Thomas L. Black, and Michael E. Baldwin

Abstract

An explicit cloud prediction scheme has been developed and incorporated into the Eta Model at the National Centers for Environmental Prediction (NCEP) to improve the cloud and precipitation forecasts. In this scheme, the cloud liquid water and cloud ice are explicitly predicted by adding only one prognostic equation of cloud mixing ratio to the model. Precipitation of rain and snow in this scheme is diagnostically calculated from the predicted cloud fields. The model-predicted clouds are also used in the model’s radiation calculations. Results from the parallel tests performed at NCEP show improvements in precipitation forecasts when prognostic cloud water is included. Compared with the diagnostic clouds, the model-predicted clouds are more accurate in both amount and position. Improvements in specific humidity forecasts have also been found, especially near the surface and above the freezing level.

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Song-You Hong, Hann-Ming Henry Juang, and Qingyun Zhao

Abstract

The purpose of this study is to develop a precipitation physics package for the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) designed to improve the skill of precipitation forecasts. The package incorporates a prognostic grid-resolvable precipitation scheme and a subgrid-scale precipitation parameterization scheme with a convective trigger that explicitly couples boundary layer and convective precipitation processes. In this paper, the implementation of a prognostic cloud scheme for the NCEP RSM is described. A subgrid-scale precipitation parameterization scheme was described in a companion paper.

Dynamical processes such as advection and diffusion processes for liquid species are included. Eleven experiments are conducted with a grid spacing of approximately 25 km for a heavy rain case over the United States during 15–17 May 1995. Special attention is given to the setup of the prognostic grid-resolvable precipitation scheme on a spectral grid as well as the importance of dynamical processes on a mesoscale grid together with radiation feedback. Different prognostic cloud schemes, classified according to the number of predicted liquid species, are also compared.

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